Hybrid Intrusion Detection System Development in Wireless Sensor Networks

Year 2021, Volume: IDAP-2021 : 5th International Artificial Intelligence and Data Processing symposium Issue: Special, 162 - 174, 20.10.2021

Hamza Elbahadır Ebubekir Erdem

https://doi.org/10.53070/bbd.990934

Abstract

Since wireless sensor networks (WSNs), which have widespread usage areas today, are different from traditional network architecture, security solutions specific to WSNs should be produced. In this study, an intrusion detection system (IDS) is proposed for WSN security. For an effective security, a hybrid model including anomaly and misuse-based detection methods used in intrusion detection system has been studied. Data mining algorithms BayesNet, J48, JRip, PART and RandomForest were used to classify the normal and attack traffic of the system and the performance values of the algorithms are shared. In this study, CSE-CIC-IDS2018, an up-to-date data set, was used, unlike the existing studies in the literature. Considering the WSN performance criteria, the data in the data set was preprocessed. The results showed that the proposed system has high accuracy.

Keywords

Wireless sensor networks, intrusion detection, anomaly, hybrid

Kablosuz Algılayıcı Ağlarda Hibrit Saldırı Tespit Sistemi Geliştirme

Abstract

Günümüzde yaygın kullanım alanlarına sahip olan kablosuz algılayıcı ağlar (KAA), geleneksel ağ mimarisinden farklı olduğundan, özgün güvenlik çözümleri üretilmelidir. Bu çalışmada KAA güvenliği için saldırı tespit sistemi (STS) önerilmiştir. Etkili bir güvenlik için, saldırı tespit sistemlerinde kullanılan anomali ve yanlış kullanım tabanlı algılama metotlarını ihtiva eden hibrit bir model üzerinde çalışılmıştır. Sistemin normal ve saldırı trafiğini sınıflandırabilmesi için veri madenciliği algoritmalarından BayesNet, J48, JRip, PART ve RandomForest algoritmaları kullanılmış ve söz konusu algoritmaların performans değerleri paylaşılmıştır. Bu çalışmada literatürdeki mevcut çalışmalardan farklı olarak, güncel bir veri seti olan CSE-CIC-IDS2018 kullanılmıştır. Veri setindeki veriler ise, KAA performans kriterleri göz önünde bulundurularak ön işleme tabi tutulmuştur. Sonuçlar önerilen sistemin yüksek doğruluk oranına sahip olduğunu göstermiştir.

Keywords

Kablosuz algılayıcı ağ, saldırı tespit, veri madenciliği, hibrit

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